text: #!/usr/bin/env python3
  # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  
  """
  Grid features pre-training script.
  
  This script is a simplified version of the training script in detectron2/tools.
  """
  
  import os
  import torch
  
  import detectron2.utils.comm as comm
  from detectron2.checkpoint import DetectionCheckpointer
  from detectron2.config import get_cfg
  from detectron2.data import MetadataCatalog
  from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
  from detectron2.evaluation import COCOEvaluator, DatasetEvaluators, verify_results
  
  from grid_feats import (
      add_attribute_config,
      build_detection_train_loader_with_attributes,
      build_detection_test_loader_with_attributes,
  )
  
  
  class Trainer(DefaultTrainer):
      """
      A trainer for visual genome dataset.
      """
      @classmethod
      def build_evaluator(cls, cfg, dataset_name, output_folder=None):
          if output_folder is None:
              output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
          evaluator_list = []
          evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
          if evaluator_type == "coco":
              return COCOEvaluator(dataset_name, cfg, True, output_folder)
          if len(evaluator_list) == 0:
              raise NotImplementedError(
                  "no Evaluator for the dataset {} with the type {}".format(
                      dataset_name, evaluator_type
                  )
              )
          if len(evaluator_list) == 1:
              return evaluator_list[0]
          return DatasetEvaluators(evaluator_list)
  
      @classmethod
      def build_train_loader(cls, cfg):
          return build_detection_train_loader_with_attributes(cfg)
  
      @classmethod
      def build_test_loader(cls, cfg, dataset_name):
          return build_detection_test_loader_with_attributes(cfg, dataset_name)
  
  
  def setup(args):
      """
      Create configs and perform basic setups.
      """
      cfg = get_cfg()
      add_attribute_config(cfg)
      cfg.merge_from_file(args.config_file)
      cfg.merge_from_list(args.opts)
      cfg.freeze()
      default_setup(cfg, args)
      return cfg
  
  
  def main(args):
      cfg = setup(args)
  
      if args.eval_only:
          model = Trainer.build_model(cfg)
          DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
              cfg.MODEL.WEIGHTS, resume=args.resume
          )
          res = Trainer.test(cfg, model)
          if comm.is_main_process():
              verify_results(cfg, res)
          return res
  
      trainer = Trainer(cfg)
      trainer.resume_or_load(resume=args.resume)
      return trainer.train()
  
  
  if __name__ == "__main__":
      args = default_argument_parser().parse_args()
      print("Command Line Args:", args)
      launch(
          main,
          args.num_gpus,
          num_machines=args.num_machines,
          machine_rank=args.machine_rank,
          dist_url=args.dist_url,
          args=(args,),
      )
  